AI for Climate Finance: Agentic Retrieval and Multi-Step Reasoning for Early Warning System Investments
Journal:
arXiv
Published Date:
Apr 7, 2025
Abstract
Tracking financial investments in climate adaptation is a complex and
expertise-intensive task, particularly for Early Warning Systems (EWS), which
lack standardized financial reporting across multilateral development banks
(MDBs) and funds. To address this challenge, we introduce an LLM-based agentic
AI system that integrates contextual retrieval, fine-tuning, and multi-step
reasoning to extract relevant financial data, classify investments, and ensure
compliance with funding guidelines. Our study focuses on a real-world
application: tracking EWS investments in the Climate Risk and Early Warning
Systems (CREWS) Fund. We analyze 25 MDB project documents and evaluate multiple
AI-driven classification methods, including zero-shot and few-shot learning,
fine-tuned transformer-based classifiers, chain-of-thought (CoT) prompting, and
an agent-based retrieval-augmented generation (RAG) approach. Our results show
that the agent-based RAG approach significantly outperforms other methods,
achieving 87\% accuracy, 89\% precision, and 83\% recall. Additionally, we
contribute a benchmark dataset and expert-annotated corpus, providing a
valuable resource for future research in AI-driven financial tracking and
climate finance transparency.
